seldon-core
domino-research
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seldon-core | domino-research | |
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14 | 3 | |
4,212 | 76 | |
1.7% | - | |
7.8 | 0.0 | |
5 days ago | about 2 years ago | |
HTML | Python | |
GNU General Public License v3.0 or later | Apache License 2.0 |
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seldon-core
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seldon-core VS MLDrop - a user suggested alternative
2 projects | 20 Feb 2023
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[D] Feedback on a worked Continuous Deployment Example (CI/CD/CT)
ZenML is an extensible, open-source MLOps framework to create production-ready machine learning pipelines. Built for data scientists, it has a simple, flexible syntax, is cloud- and tool-agnostic, and has interfaces/abstractions that are catered towards ML workflows. Seldon Core is a production grade open source model serving platform. It packs a wide range of features built around deploying models to REST/GRPC microservices that include monitoring and logging, model explainers, outlier detectors and various continuous deployment strategies such as A/B testing, canary deployments and more.
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[D] BentoML's Compatibility with Seldon;
I am using BentoML to build the docker container for a BERT model, and then deploy that using Seldon on GKE. The model's REST API endpoint works fine. at terms of compatibility with Seldon, the metrics are being scraped by Prometheus and visualized on Grafana. The only Seldon component that doesn't appear to be working is the request logging, which I have working for other applications that were deployed on Seldon. I am using the elastic stack from here. From my understanding, request logging should still be compatible and the β only lost functionality should be Seldon's model metadata. Any insight on how to get the centralized request logging working? No errors were shown; it's just that the logs aren't being captured and sent to ElasticSearch. Anyone have any success using BentoML with Seldon and not losing any of Seldon's features?
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Building a Responsible AI Solution - Principles into Practice
While tools in the model experimentation space normally include diagnostic charts on a model's performance, there are also specialised solutions that help ensure that the deployed model continues to perform as they are expected to. This includes the likes of seldon-core, why-labs and fiddler.ai.
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Ask HN: Who is hiring? (January 2022)
Seldon | Multiple positions | London/Cambridge UK | Onsite/Remote | Full time | seldon.io
At Seldon we are building industry leading solutions for deploying, monitoring, and explaining machine learning models. We are an open-core company with several successful open source projects like:
* https://github.com/SeldonIO/seldon-core
* https://github.com/SeldonIO/mlserver
* https://github.com/SeldonIO/alibi
* https://github.com/SeldonIO/alibi-detect
* https://github.com/SeldonIO/tempo
We are hiring for a range of positions, including software engineers(go, k8s), ml engineers (python, go), frontend engineers (js), UX designer, and product managers. All open positions can be found at https://www.seldon.io/careers/
- Ask HN: Who is hiring? (December 2021)
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Has anyone implemented Seldon?
Also note our github repo has a link to our slack where you can ask active users: https://github.com/SeldonIO/seldon-core
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[Discussion] Look for service to upload a model and receive a REST API endpoint, for serving predictions
If you want to serve your model at scale, with a bunch of production features you should have a look at the open-source framework Seldon Core. It does what you're asking for plus a bunch of other cool stuff like routing, logging and monitoring.
- Seldon Core : Open-source platform for rapidly deploying machine learning models on Kubernetes
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Looking for open-source model serving framework with dashboard for test data quality
Seldon ticks most of those boxes if you already have some experience with kubernetes. You can set up a/b tests, do payload logging to elastic and then do monitoring on top of that, and it has drift detection and model explainer modules too. Idk about great expectations integration, but you could probably do something with a custom transformer module as part of the inference graph.
domino-research
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[N] Open Sourcing Checkpoint π
Here is a direct URL: https://github.com/dominodatalab/domino-research/tree/main/checkpoint
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Open-sourcing Bridge π
The Domino R&D team is open-sourcing Bridge, a tool that turns your model registry into the declarative source-of-truth for model deployment and hosting.
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[Discussion] Look for service to upload a model and receive a REST API endpoint, for serving predictions
(Disclosure, I am a maintainer on this project) You should checkout Bridge - it deploys models directly from an MLflow registry to SageMaker inference endpoints (hosted APIs). It basically turns your registry into a declarative source of truth for your hosting. The advantage of this approach is that it provides a clean way to update/upgrade your APIs from the same place you're tracking your new versions, experiments etc. One source of truth. You can get an MLflow registry up in a couple minutes if you don't have one.
What are some alternatives?
BentoML - The most flexible way to serve AI/ML models in production - Build Model Inference Service, LLM APIs, Inference Graph/Pipelines, Compound AI systems, Multi-Modal, RAG as a Service, and more!
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
MLServer - An inference server for your machine learning models, including support for multiple frameworks, multi-model serving and more
transformers - π€ Transformers: State-of-the-art Machine Learning for Pytorch, TensorFlow, and JAX.
evidently - Evaluate and monitor ML models from validation to production. Join our Discord: https://discord.com/invite/xZjKRaNp8b
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
great_expectations - Always know what to expect from your data.
huggingface_hub - The official Python client for the Huggingface Hub.
alibi-detect - Algorithms for outlier, adversarial and drift detection
Activeloop Hub - Data Lake for Deep Learning. Build, manage, query, version, & visualize datasets. Stream data real-time to PyTorch/TensorFlow. https://activeloop.ai [Moved to: https://github.com/activeloopai/deeplake]